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THIS RESOURCE IS NO LONGER IN SERVICE, documented on July 16, 2013. A built-in toolbox for the tracing and analysis of neuroanatomy from nanoscale (high-resolution) imaging. It is a project under ongoing development. The name is originating by merging the words Neuron + reconstruct. The working concept is organized in filters applied successively on the image stack to be processed (pipeline). Currently, the focus of the software is the extraction of detailed neuroanatomical profiles from nanoscale imaging techniques, such as the Serial Block-Face Scanning Electron Microscopy (SBFSEM). The techniques applied, however, may be used to analyze data from various imaging methods and neuronal versatility. The underlying idea of Neurostruct is the use of slim interfaces/filters allowing an efficient use of new libraries and data streaming. The image processing follows in voxel pipelines by using the CUDA programming model and all filters are programmed in a datasize-independent fashion. Thus Neurostruct exploits efficiency and datasize-independence in an optimal way. Neurostruct is based on the following main principles: * Image processing in voxel pipelines using the general purpose graphics processing units (GPGPU) programming model. * Efficient implementation of these interfaces. Programming model and image streaming that guarantees a minimal performance penalty. * Datasize-independent programming model enabling independence from the processed image stack. * Management of the filters and IO data through shell scripts. The executables (filters) are currently managed through shell scripts. The application focuses currently in the tracing of single-biocytin filled cells using SBFSEM imaging. : * Extraction of neuroanatomical profiles: 3D reconstrution and 1D skeletons of the imaged neuronal structure. * Complete tracing: Recognition of the full neuronal structure using envelope techniques, thereby remedying the problem of spines with thin necks of an internal diameter approaching the SBFSEM resolution. * Separation (Coloring) of subcellular structures: Algorithms for the separation of spines from their root dendritic stem. * Evaluation and analysis of the imaged neuroanatomy: Calculation of the dendritic and spine membrane''s surface, spine density and variation, models of dendrites and spines
Proper citation: Neurostruct (RRID:SCR_008861) Copy
http://bioinf.cs.ucl.ac.uk/psipred/
Web tool as secondary structure prediction method, incorporating two feed forward neural networks which perform analysis on output obtained from PSI-BLAST. Web server offering analyses of protein sequences.
Proper citation: PSIPRED (RRID:SCR_010246) Copy
Software tool to identify known and novel miRNA genes in seven animal clades by analyzing sequenced RNAs. Used for discovering known and novel miRNAs from small RNA sequencing data.
Proper citation: miRDeep (RRID:SCR_010829) Copy
Core facility provides researchers with access to high-throughput sequencing technologies. The staff provide consultation on experimental design, library preparation, and data analysis. The Sequencing Core Facility works closely with Bioinformatics staff in the Center for Quantitative Biology to provide researchers with computing power and consulting services to analyze sequencing data.
Proper citation: Princeton High Throughput Sequencing and Microarray Facility (RRID:SCR_012619) Copy
Data sharing repository of clinical trials, associated mechanistic studies, and other basic and applied immunology research programs. Platform to store, analyze, and exchange datasets for immune mediated diseases. Data supplied by NIAID/DAIT funded investigators and genomic, proteomic, and other data relevant to research of these programs extracted from public databases. Provides data analysis tools and immunology focused ontology to advance research in basic and clinical immunology.
Proper citation: The Immunology Database and Analysis Portal (ImmPort) (RRID:SCR_012804) Copy
https://github.com/macs3-project/MACS
Software Python package for identifying transcript factor binding sites. Used to evaluate significance of enriched ChIP regions. Improves spatial resolution of binding sites through combining information of both sequencing tag position and orientation. Can be used for ChIP-Seq data alone, or with control sample with increase of specificity.
Proper citation: MACS (RRID:SCR_013291) Copy
http://www.mrc-lmb.cam.ac.uk/genomes/dolop/
DOLOP is an exclusive knowledge base for bacterial lipoproteins by processing information from 510 entries to provide a list of 199 distinct lipoproteins with relevant links to molecular details. Features include functional classification, predictive algorithm for query sequences, primary sequence analysis and lists of predicted lipoproteins from 43 completed bacterial genomes along with interactive information exchange facility. This website along will have additional information on the biosynthetic pathway, supplementary material and other related figures. DOLOP also contains information and links to molecular details for about 278 distinct lipoproteins and predicted lipoproteins from 234 completely sequenced bacterial genomes. Additionally, the website features a tool that applies a predictive algorithm to identify the presence or absence of the lipoprotein signal sequence in a user-given sequence. The experimentally verified lipoproteins have been classified into different functional classes and more importantly functional domain assignments using hidden Markov models from the SUPERFAMILY database that have been provided for the predicted lipoproteins. Other features include: primary sequence analysis, signal sequence analysis, and search facility and information exchange facility to allow researchers to exchange results on newly characterized lipoproteins.
Proper citation: DOLOP: A Database of Bacterial Lipoproteins (RRID:SCR_013487) Copy
http://www.cma.mgh.harvard.edu/iatr/display.php?spec=id&ids=107
THIS RESOURCE IS NO LONGER IN SERVICE. Documented on June 6,2023. EMMA (Extensible MATLAB Medical image Analysis) is a toolkit designed to ease the use of MATLAB in the analysis of medical imaging data. It provides functions for reading and writing MINC files, viewing images, performing ROI operations, and performing several popular analyses. Also, there are toolkits for performing kinetic analysis of dynamic PET rCBF (regional cerebral blood flow) and FDG data. The goal for this site is to provide a centrally available listing of all image analysis tools that are available to the neuroscience community in order to facilitate the development, identification, and sharing of tools that are of use to the general community.
Proper citation: Extensible MATLAB Medical image Analysis (RRID:SCR_013499) Copy
http://www.biotech.uconn.edu/resources/biophysics
Software for analyzing sedimentation equilibrium (SE) data from analytical ultracentrifugation experiments. It uses a model-dependent simulation of data for matching data in order to determine when equilibrium has been achieved.
Proper citation: HeteroAnalysis (RRID:SCR_014991) Copy
Web server for statistical, functional and integrative analysis of metabolomics data. Web based tool suite used for metabolomic data processing, normalization, multivariate statistical analysis, and data annotation, biomarker discovery and classification.
Proper citation: MetaboAnalyst (RRID:SCR_015539) Copy
https://www.ncbi.nlm.nih.gov/pubmed/23489480
Software package for image analysis to determine the orientation of filamentous structures on digital images. Used as an image‐processing tool for analyzing cytoskeleton and cellulose fiber orientation in pant imagesk.
Proper citation: MicroFilament Analyzer (RRID:SCR_016411) Copy
https://fractalis.lcsb.uni.lu/
Software as a scalable open-source service for platform-independent interactive visual analysis of biomedical data. It is a service and a library that work in tandem to equip existing platforms with visual analytical capabilities for explorative data analysis.
Proper citation: Fractalis (RRID:SCR_016362) Copy
http://cole-trapnell-lab.github.io/monocle-release/docs/
Software package for analyzing single cell gene expression, classifying and counting cells, performing differential expression analysis between subpopulations of cells, and reconstructing cellular trajcectories. Works well with very large single-cell RNA-Seq experiments containing tens of thousands of cells or more. Used in computational analysis of gene expression data in single cell gene expression studies to profile transcriptional regulation in complex biological processes and highly heterogeneous cell populations.
Proper citation: Monocle2 (RRID:SCR_016339) Copy
https://github.com/KM-Lab/Electrographic-Seizure-Analyzer
Software to automate analysis of electrographic seizures based on EEG or LFP data, featuring customizable thresholds and parameters for event detection and parameter setting.
Proper citation: Electrographic Seizure Analyzer (RRID:SCR_016344) Copy
https://amp.pharm.mssm.edu/biojupies/
Software as an open source web server that automatically generates RNA-seq data analysis of jupyter notebooks. It allows creation and containment of documents that have live code, visualizations and narrative text.
Proper citation: BioJupies (RRID:SCR_016346) Copy
https://bioconductor.org/packages/release/bioc/html/MAST.html
Software as an open source package for assessing transcriptional changes and characterizing heterogeneity in single-cell RNA sequencing data.
Proper citation: MAST (RRID:SCR_016340) Copy
Python library for materials analysis codes. Defines core object representations for structures and molecules.
Proper citation: Pymatgen (RRID:SCR_016565) Copy
Software tool for assay data analysis.
Proper citation: MyAssays (RRID:SCR_016562) Copy
Web application for integrated analysis and interactive visualization of RNA interference (RNAi) screening data.
Proper citation: CARD (RRID:SCR_016602) Copy
http://imagej.net/Simple_Neurite_Tracer
Software tool for reconstruction, visualization and analysis of neuronal processes .Fiji's framework for semi-automated tracing of neurons and other tube-like structures (blood vessels) through 3D image stacks.
Proper citation: Simple Neurite Tracer (RRID:SCR_016566) Copy
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